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Model: mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+tags:
+  - biology
+  - bio-posttrain
+  - dna-sft
+  - dna
+library_name: transformers
+---
+
+# Bio-posttrain Qwen3-1.7B DNA SFT
+
+DNA supervised fine-tuning (SFT) checkpoint from [How Post-Training Shapes Biological Reasoning Models](https://huggingface.co/collections/mims-harvard/bio-posttrain).
+
+## Model details
+
+- **Base model:** `Qwen/Qwen3-1.7B`
+- **DNA encoder:** Evo2 `evo2_1b_base` (frozen; not included in this repo)
+- **Embedding layer:** `blocks.20.mlp.l3`
+- **LoRA:** rank 64, alpha 128
+- **Validation loss:** 0.4687
+
+This repo contains the **merged text LLM** (LoRA fused into base weights) plus `dna_projection.pt`.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `model.safetensors` | Merged Qwen3-1.7B weights |
+| `dna_projection.pt` | Linear map from Evo2 hidden (1920) → text hidden (2048) |
+| `dna_model_config.json` | DNA encoder + projection metadata |
+
+## Loading
+
+Use the [BioReason](https://github.com/mims-harvard/BioReason) `DNALLMModel` loader:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+text_model = AutoModelForCausalLM.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft")
+tokenizer = AutoTokenizer.from_pretrained("mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft")
+proj = torch.load("mims-harvard/bio-posttrain-qwen3-1.7b-dna-sft/dna_projection.pt", map_location="cpu")
+# Load Evo2 separately: evo2_1b_base
+```
+
+See `dna_model_config.json` for encoder settings.
+
+## Collection
+
+Part of the [Bio-posttrain](https://huggingface.co/collections/mims-harvard/bio-posttrain) collection.